SBIR Phase II: An adaptive machine learning-based platform to improve surgical quality and patient outcomes

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将是在医疗保健环境向基于价值的医疗转变的过程中，帮助引入个性化和量身定制的手术护理。医院和外科医生正在寻求解决办法，使他们能够有针对性地（而不是泛泛地）改进手术质量，以提高患者的治疗效果和有效利用资源。通过主动识别手术风险并为患者匹配最适合这些风险层的干预措施，拟议的技术旨在支持医院实现其基于价值的护理目标。更大的愿景是通过利用人工智能和机器学习进行预测、主动干预和封闭反馈回路中的结果跟踪，将这一范式应用于所有医学领域。在外科等高成本、高风险的专业中证明这一点，为将该技术扩展到其他医学专业并服务于更大的国内和国际市场提供了一条途径。最终，从该技术的广泛使用中吸取的经验教训将使社会能够获得应用数据科学、预防医学和医院企业解决方案的技术可扩展性方面的关键知识核心。该项目是推动医疗技术临界点所需的关键活动的跨学科代表。这个小企业创新研究（SBIR）第二阶段项目建立在第一阶段的成果之上，其中包括预测引擎开发、可扩展数据处理管道开发和医院利益相关者参与活动。第二阶段的工作重点是进一步开发该技术，以促进其商业应用和临床环境的整合。二期项目的主要目标如下：(1)开发应用程序编程接口（API），为满足不同需求的广大用户提供量身定制的机器学习模型；(2)扩展由多个外科专科临床证据支持的临床干预库；(3)开发结果仪表板，通过自动提取电子健康记录显示术后患者结果。该项目的结果将是一个闭环的临床和技术基础设施，能够灵活地满足各种手术客户的需求，从而提高整个手术生态系统的质量。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to help usher in personalized and tailored surgical care within a shifting healthcare context toward value-based care. Hospitals and surgeons are seeking solutions that will enable them to target, as opposed to generalizing, improvements in surgical quality for enhanced patient outcomes and effective use of resources. By proactively identifying surgical risks and matching patients to interventions most appropriate for these risk strata, the proposed technology is designed to support hospitals in meeting their value-based care objectives. The larger vision is to apply this paradigm in all of medicine by leveraging Artificial Intelligence and Machine Learning for prediction, proactive intervention, and outcomes tracking in a closed feedback loop. Demonstrating this in a high-cost, high-risk specialty like surgery provides a path for expanding the technology into other medical specialties and serving a greater domestic and international market. Ultimately, the lessons learned from the wide-spread use of this technology will allow society to derive key kernels of knowledge in applied data science, preventative medicine, and technical scalability of hospital enterprise solutions. This project is an interdisciplinary representation of crucial activities needed to drive the tipping point of medical technology. This Small Business Innovation Research (SBIR) Phase II project builds upon the results of Phase I, which included predictive engine development, scalable data processing pipeline development, and hospital stakeholder engagement activities. Phase II efforts focus on further developing the technology to facilitate its commercial use and integration in clinical settings. Key objectives for the Phase II project are as follows: (1) development of an Application Programming Interface (API) to deliver tailored machine learning models to broad users across varying needs, (2) expansion of a clinical intervention library supported by clinical evidence across multiple surgical specialties, and (3) development of an outcomes dashboard to display postoperative patient outcomes from automated extraction of electronic health records. The result of this project will be a closed-loop clinical and technical infrastructure that is agile to the needs of a diverse range of surgical customers to enable quality improvement across an entire surgical ecosystem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.